Automatic information extraction from remote sensing images and 3D point clouds for building damage assessment

2018 ◽  
Author(s):  
A. Vetrivel
Keyword(s):  
Remote Sensing ◽  
Information Extraction ◽  
Damage Assessment ◽  
Point Clouds ◽  
Building Damage ◽  
Remote Sensing Images ◽  
3D Point Clouds ◽  
Automatic Information ◽  
Automatic Information Extraction

scholarly journals U-net Network for Building Information Extraction of Remote-Sensing Imagery

2018 ◽  
Vol 14 (12) ◽  
pp. 179
Author(s):  
Jingtan Li ◽  
Maolin Xu ◽  
Hongling Xiu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Information Extraction ◽  
Image Data ◽  
Semantic Segmentation ◽  
Remote Sensing Image ◽  
Remote Sensing Images ◽  
Training Set ◽  
Building Information ◽  
The Face

With the resolution of remote sensing images is getting higher and higher, high-resolution remote sensing images are widely used in many areas. Among them, image information extraction is one of the basic applications of remote sensing images. In the face of massive high-resolution remote sensing image data, the traditional method of target recognition is difficult to cope with. Therefore, this paper proposes a remote sensing image extraction based on U-net network. Firstly, the U-net semantic segmentation network is used to train the training set, and the validation set is used to verify the training set at the same time, and finally the test set is used for testing. The experimental results show that U-net can be applied to the extraction of buildings.

scholarly journals Spatiotemporal Fusion in Remote Sensing

2020 ◽  
Author(s):  
Hessah Albanwan ◽  
Rongjun Qin
Keyword(s):  
Remote Sensing ◽  
Quality Improvement ◽  
Data Quality ◽  
Information Extraction ◽  
Remote Sensing Images ◽  
Sensing Applications ◽  
Surface Data ◽  
Remote Sensing Applications ◽  
Basic Concepts ◽  
Free Set

Remote sensing images and techniques are powerful tools to investigate earth’s surface. Data quality is the key to enhance remote sensing applications and obtaining clear and noise-free set of data is very difficult in most situations due to the varying acquisition (e.g., atmosphere and season), sensor and platform (e.g., satellite angles and sensor characteristics) conditions. With the increasing development of satellites, nowadays Terabytes of remote sensing images can be acquired every day. Therefore, information and data fusion can be particularly important in the remote sensing community. The fusion integrates data from various sources acquired asynchronously for information extraction, analysis, and quality improvement. In this chapter, we aim to discuss the theory of spatiotemporal fusion by investigating previous works, in addition to describing the basic concepts and some of its applications by summarizing our prior and ongoing works.

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